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It's never been a better time to be creative. (ever) The two best frontier models (ever) have been released within 30 days of each other. Here’s what we learned from running 4 frontier models head to head. >Sol has taste and Fable takes direction. 10 identical landing page briefs,... show more

ben

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35,609 次观看 • 1 个月前 •via X (Twitter)

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Fable is back (enjoy the final hours of in-sub usage) so we made it fight Anthropic's Opus 4.8: >5 real landing page and portfolio briefs, built in Claude Code, >judged blind by 9 working designers across 90 matchups. Fable's best brief won 88.9% of its head-to-heads. Its worst output only won 11.1%. The difference was the brief 🧵

Fable is back (enjoy the final hours of in-sub usage) so we made it fight Anthropic's Opus 4.8: >5 real landing page and portfolio briefs, built in Claude Code, >judged blind by 9 working designers across 90 matchups. Fable's best brief won 88.9% of its head-to-heads. Its worst output only won 11.1%. The difference was the brief 🧵

ben

104,624 次观看 • 1 个月前

Grok 4.5 performed GPT Sol level for free! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: -robot deathmatch, Tombstone vs Minotaur -a hydraulic press flattening stuff on a conveyor -a semi truck jumping a canyon Outputs: GPT-5.6 Sol: 12.9K tokens, $0.51 (~7 min) Grok 4.5: 10.8K tokens, $0 (~5 min) Muse Spark 1.1: 26.8K tokens, $0.12 (~7.5 min) GLM 5.2: 10.9K tokens, $0.02 (~12 min) Grok 4.5 handled all three scenes genuinely well and got surprisingly close to GPT-5.6 this round. On top of that, it ran on the free tier. GPT-5.6 Sol, the frontier model, put out solid but not standout work. GLM 5.2 rendered all three scenes for pennies, but it came out the roughest of the four. Meta's new Muse Spark burned the most tokens yet still stayed cheap, delivering an average result.

Grok 4.5 performed GPT Sol level for free! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: -robot deathmatch, Tombstone vs Minotaur -a hydraulic press flattening stuff on a conveyor -a semi truck jumping a canyon Outputs: GPT-5.6 Sol: 12.9K tokens, $0.51 (~7 min) Grok 4.5: 10.8K tokens, $0 (~5 min) Muse Spark 1.1: 26.8K tokens, $0.12 (~7.5 min) GLM 5.2: 10.9K tokens, $0.02 (~12 min) Grok 4.5 handled all three scenes genuinely well and got surprisingly close to GPT-5.6 this round. On top of that, it ran on the free tier. GPT-5.6 Sol, the frontier model, put out solid but not standout work. GLM 5.2 rendered all three scenes for pennies, but it came out the roughest of the four. Meta's new Muse Spark burned the most tokens yet still stayed cheap, delivering an average result.

atomic.chat

70,490 次观看 • 1 个月前

How much better are the internal, unreleased models at frontier labs like Google, OpenAI, and Anthropic? We got a glimpse exactly one year ago today, when Google accidentally leaked the “Kingfall” model "Kingfall" was likely an unreleased Gemini 2.5 Ultra-sized model. It was available in AI Studio for only a few minutes but remained accessible through the API for several days At the time, "Kingfall" appeared to be significantly better than Gemini 2.5 Pro at both code generation and creative writing In a recent interview, Sundar Pichai mentioned that Google could have made a better, Ultra-sized Gemini Omni model, but would have had trouble serving it The infrastructure required to serve Ultra-sized models at scale is likely why Google never publicly released models like “Kingfall”

How much better are the internal, unreleased models at frontier labs like Google, OpenAI, and Anthropic? We got a glimpse exactly one year ago today, when Google accidentally leaked the “Kingfall” model "Kingfall" was likely an unreleased Gemini 2.5 Ultra-sized model. It was available in AI Studio for only a few minutes but remained accessible through the API for several days At the time, "Kingfall" appeared to be significantly better than Gemini 2.5 Pro at both code generation and creative writing In a recent interview, Sundar Pichai mentioned that Google could have made a better, Ultra-sized Gemini Omni model, but would have had trouble serving it The infrastructure required to serve Ultra-sized models at scale is likely why Google never publicly released models like “Kingfall”

AiBattle

12,010 次观看 • 2 个月前

GPT-5.6 Sol is unbelievably good at creating and editing videos. It can do motion design, product demos, and animations like this one I made by simply giving it a screen recording. GPT 5.6 has the best design taste and significantly outperforms Fable, which relies heavily on repetitive design patterns. To help you experiment with video editing on it, we just launched a collection of 100 ready-to-use skills that show what’s possible and help you get started with video editing using GPT-5.6. These skills can create anything from motion graphics launch videos for your product to a 3B1B-style science explainer video. You can also use them to edit existing videos: add captions, generate motion graphics, create voiceovers, redesign visual styles, translate into new languages, and much more. If you want access to the full library, comment “VIDEO SKILLS” and I’ll share it with you. (You'll have to follow me so I can DM you.)

GPT-5.6 Sol is unbelievably good at creating and editing videos. It can do motion design, product demos, and animations like this one I made by simply giving it a screen recording. GPT 5.6 has the best design taste and significantly outperforms Fable, which relies heavily on repetitive design patterns. To help you experiment with video editing on it, we just launched a collection of 100 ready-to-use skills that show what’s possible and help you get started with video editing using GPT-5.6. These skills can create anything from motion graphics launch videos for your product to a 3B1B-style science explainer video. You can also use them to edit existing videos: add captions, generate motion graphics, create voiceovers, redesign visual styles, translate into new languages, and much more. If you want access to the full library, comment “VIDEO SKILLS” and I’ll share it with you. (You'll have to follow me so I can DM you.)

Akash Anand

513,224 次观看 • 1 个月前

Here’s a real life example of what we have talked about over the last couple of days. Spending all of your time studying limited assets so that you become really good at trading a select 1-2 assets. Live in those charts you’ll get to know them intimately well. My main two are SOL and ETH. But I haven’t traded ETH in a few weeks. It shows on my entry. Lesson in that.

Here’s a real life example of what we have talked about over the last couple of days. Spending all of your time studying limited assets so that you become really good at trading a select 1-2 assets. Live in those charts you’ll get to know them intimately well. My main two are SOL and ETH. But I haven’t traded ETH in a few weeks. It shows on my entry. Lesson in that.

The White Whale

74,221 次观看 • 9 个月前

ANNOUNCEMENT TIME. . TOM A SMITH X SWIM SCHOOL I DONT WANT YOU TO HAVE TO REMEMBER ME FOR LONGER THAN YOU EVER KNEW ME Can I start by saying that this is the best song I’ve ever written. When we play it live it’s had the best reaction I’ve ever had and it’s been requested almost daily since we first played it. It’s also the longest title ever! . I wrote it with a female co-harmony in mind and after a chance meeting at Isle of Wight Festival the amazing Swim School agreed to collaborate. Can I just say that Alice is one of the most creative, innovative performers I have ever met. We ended up with layer upon layer of her incredible vocals that completely dominate the song and take it to another level. What a band man. . . I can’t wait for you to hear it but you’ll need to wait until 30/8.

ANNOUNCEMENT TIME. . TOM A SMITH X SWIM SCHOOL I DONT WANT YOU TO HAVE TO REMEMBER ME FOR LONGER THAN YOU EVER KNEW ME Can I start by saying that this is the best song I’ve ever written. When we play it live it’s had the best reaction I’ve ever had and it’s been requested almost daily since we first played it. It’s also the longest title ever! . I wrote it with a female co-harmony in mind and after a chance meeting at Isle of Wight Festival the amazing Swim School agreed to collaborate. Can I just say that Alice is one of the most creative, innovative performers I have ever met. We ended up with layer upon layer of her incredible vocals that completely dominate the song and take it to another level. What a band man. . . I can’t wait for you to hear it but you’ll need to wait until 30/8.

TOM A SMITH

18,992 次观看 • 2 年前

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.

Alok

65,387 次观看 • 1 个月前

Claude "Puzzling" while GPT 5.6 on GOD-MODE just bade a banger that's mindblowing. Here's the exact way to get a site like this, step by step: > open the desktop app, pick Sol, reasoning on High. taste work never goes to small models > drop it 3 sites with motion you love and one line: "reverse-engineer the art direction: mood, typography, pacing, and WHY each animation exists. save it as a style bible" > brief in one paragraph, goal not steps: "[your niche] site, cinematic scroll, every animation has a job. follow the bible" > house rules on top: no template hero, no stock gradients, nothing on the page moves without a reason > now the bar: "a motion designer can't tell this from an agency build." spin up a SECOND 5.6 with fresh context whose only job is to FAIL the build against that bar > /loop overnight: build, grade, close the biggest gap, again. you're asleep for all of it > when the verifier runs out of complaints: tag Sites. live URL, one click, zero hosting The deeper version of every step (the full contract, the house rules, the verifier trick, when Ultra is worth the bill) is in the article below. P.S. send the article to your GPT and tell it "we're doing this tonight".

Claude "Puzzling" while GPT 5.6 on GOD-MODE just bade a banger that's mindblowing. Here's the exact way to get a site like this, step by step: > open the desktop app, pick Sol, reasoning on High. taste work never goes to small models > drop it 3 sites with motion you love and one line: "reverse-engineer the art direction: mood, typography, pacing, and WHY each animation exists. save it as a style bible" > brief in one paragraph, goal not steps: "[your niche] site, cinematic scroll, every animation has a job. follow the bible" > house rules on top: no template hero, no stock gradients, nothing on the page moves without a reason > now the bar: "a motion designer can't tell this from an agency build." spin up a SECOND 5.6 with fresh context whose only job is to FAIL the build against that bar > /loop overnight: build, grade, close the biggest gap, again. you're asleep for all of it > when the verifier runs out of complaints: tag Sites. live URL, one click, zero hosting The deeper version of every step (the full contract, the house rules, the verifier trick, when Ultra is worth the bill) is in the article below. P.S. send the article to your GPT and tell it "we're doing this tonight".

Miraqle

206,109 次观看 • 1 个月前

I tried explaining to some of our American friends and family members about the sheer magnitude of fireworks being launched throughout the city of Berlin on New Year’s Eve. It is incomprehensible. When we moved here, a fellow American warned me, “Think of the biggest Fourth of July celebration you’ve ever been to and then multiply that by 10 and imagine it lasting 5 hours.” And even that doesn’t begin to describe what New Year’s Eve is like here. 90% of Germany’s fireworks are sold in 3 days, every street becomes a launchpad, and anyone can be a pyrotechnician. 🎆 Here’s our view from last year. And it went on and on for hours.

I tried explaining to some of our American friends and family members about the sheer magnitude of fireworks being launched throughout the city of Berlin on New Year’s Eve. It is incomprehensible. When we moved here, a fellow American warned me, “Think of the biggest Fourth of July celebration you’ve ever been to and then multiply that by 10 and imagine it lasting 5 hours.” And even that doesn’t begin to describe what New Year’s Eve is like here. 90% of Germany’s fireworks are sold in 3 days, every street becomes a launchpad, and anyone can be a pyrotechnician. 🎆 Here’s our view from last year. And it went on and on for hours.

Celia

2,394,213 次观看 • 7 个月前

🗓️ The Portals State of the Union is happening on Sunday, January 28, where we begin to take the wraps off what we’ve been cooking over the last half of 2023. We have been strategically quieter on the timeline, while relentlessly focused on building a self-sustaining, successful platform that thrives with both web2 and web3 audiences. We have been validating secret projects, while competitors seem to be continuing in the wrong direction. The team has been working on the most valuable experiences and products possible now that we have the fastest/best-in-class 3D experience creator out there. Portals has always been a labor of love. It’s our life’s work, and we are not going anywhere. Solana is also here to stay, further establishing itself as a force to be reckoned with and gaining more of the recognition it deserves. It's time for us to put forward more of the on-chain ecosystem projects we have always wanted to do. Rolling out new systems, incentives, and experiences to fortify Portals as one web3's killer apps and top destinations. We have a lot to share, and we can't wait to show you. This year's State of the Union will be delivered in video format, across X, Discord, and in Portals. We'll be dropping more teasers while looking forward to seeing you on January 28!

🗓️ The Portals State of the Union is happening on Sunday, January 28, where we begin to take the wraps off what we’ve been cooking over the last half of 2023. We have been strategically quieter on the timeline, while relentlessly focused on building a self-sustaining, successful platform that thrives with both web2 and web3 audiences. We have been validating secret projects, while competitors seem to be continuing in the wrong direction. The team has been working on the most valuable experiences and products possible now that we have the fastest/best-in-class 3D experience creator out there. Portals has always been a labor of love. It’s our life’s work, and we are not going anywhere. Solana is also here to stay, further establishing itself as a force to be reckoned with and gaining more of the recognition it deserves. It's time for us to put forward more of the on-chain ecosystem projects we have always wanted to do. Rolling out new systems, incentives, and experiences to fortify Portals as one web3's killer apps and top destinations. We have a lot to share, and we can't wait to show you. This year's State of the Union will be delivered in video format, across X, Discord, and in Portals. We'll be dropping more teasers while looking forward to seeing you on January 28!

Portals

37,526 次观看 • 2 年前

i generated this video using two blurry pics of myself from two years ago at griffith observatory with the MiniMax H3 model. it's surprisingly natural and sounds really close to how i actually speak. i haven't followed video generation models enough in the past year, but i'm really curious about the tech behind them. now they can even generate high-fidelity voice, accurate text, and editable video. i still remember the days when we were shocked by gpt-4o and the sora sample video. it's been a long way since then. #MiniMaxH3 MiniMax Design (H3)

i generated this video using two blurry pics of myself from two years ago at griffith observatory with the MiniMax H3 model. it's surprisingly natural and sounds really close to how i actually speak. i haven't followed video generation models enough in the past year, but i'm really curious about the tech behind them. now they can even generate high-fidelity voice, accurate text, and editable video. i still remember the days when we were shocked by gpt-4o and the sora sample video. it's been a long way since then. #MiniMaxH3 MiniMax Design (H3)

Asuka Zheng🎀

20,572 次观看 • 14 天前

🚨GAUTAM GAMBHIR MASSIVE STATEMENT ON HIS FUTURE AS INDIA's HEAD COACH🤯 Gautam Gambhir said :🗣️ "I am here to display the results & frankly speaking it has been a roller coaster ride so far. The main goal is always to win major tournaments & team has won Champions trophy & T20 World cup. There will be bad days offcourse, but this is how it goes sonedays. I don't listen to the outside noise & we have set our plans for the 2027 ODI WC. This is our first preference & then you can decide my future. I have been appointed as the coach of Indian cricket team to make it the best team in the world & we have been one of the most consistent teams in white ball cricket for some time now. We are in a transition phase in red ball cricket, and the team will get better with time".

🚨GAUTAM GAMBHIR MASSIVE STATEMENT ON HIS FUTURE AS INDIA's HEAD COACH🤯 Gautam Gambhir said :🗣️ "I am here to display the results & frankly speaking it has been a roller coaster ride so far. The main goal is always to win major tournaments & team has won Champions trophy & T20 World cup. There will be bad days offcourse, but this is how it goes sonedays. I don't listen to the outside noise & we have set our plans for the 2027 ODI WC. This is our first preference & then you can decide my future. I have been appointed as the coach of Indian cricket team to make it the best team in the world & we have been one of the most consistent teams in white ball cricket for some time now. We are in a transition phase in red ball cricket, and the team will get better with time".

Akshat

90,704 次观看 • 11 天前

This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!

This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!

Anshu

178,878 次观看 • 1 个月前

GPT-5.5 is MUCH more reliable on longer running tasks - for the first time with any model. As we speak I have a migration running for over 7+ hours - this literally never happened before, the models would maybe run for 30 mins or of you really shout at them for 2-3 hours. Last night I went to sleep, set a long running task, then queued up 10 prompts to 'keep it going'. It did not stop after the first prompt and kept going for 8+ hours and I woke up to all the same prompts still queued up. The ability to run for a long time, in combination with ability to validate with computer use & other tools, makes it much more useful for building real applications.

GPT-5.5 is MUCH more reliable on longer running tasks - for the first time with any model. As we speak I have a migration running for over 7+ hours - this literally never happened before, the models would maybe run for 30 mins or of you really shout at them for 2-3 hours. Last night I went to sleep, set a long running task, then queued up 10 prompts to 'keep it going'. It did not stop after the first prompt and kept going for 8+ hours and I woke up to all the same prompts still queued up. The ability to run for a long time, in combination with ability to validate with computer use & other tools, makes it much more useful for building real applications.

Peter Gostev

105,642 次观看 • 3 个月前

Today, we're launching the Runway Game Worlds Beta. Over the last few months, we have been working on research and products that are moving us closer toward a future where you will be able to explore any character, story or world in real time. While generating the pixels of these experiences is one aspect of this new frontier, another is the need for novel mechanics and interfaces. From how stories unfold to how your choices affect the worlds you’re simulating. Today’s beta release marks a first step in this direction, learn more below. (1/5)

Today, we're launching the Runway Game Worlds Beta. Over the last few months, we have been working on research and products that are moving us closer toward a future where you will be able to explore any character, story or world in real time. While generating the pixels of these experiences is one aspect of this new frontier, another is the need for novel mechanics and interfaces. From how stories unfold to how your choices affect the worlds you’re simulating. Today’s beta release marks a first step in this direction, learn more below. (1/5)

Runway

118,827 次观看 • 11 个月前

“The Last Meatball” Animation: jboogx.creative & enigmatic_e Movement: jboogx.creative & enigmatic_e Reference Imagery: FLUX on Civitai Jboogx and I have known each other for the better part of the last 12 months. I think we've both been inspired by each other's work and dedication to exploring all the ways we can push these tools to their limits for VFX and animation. We didn't want to force a collaboration, but recently, Jboogx was inspired by the work I've been doing and how I incorporate myself into my animations. This idea is a playful take on 'Atlas carrying the boulder,' but with a twist—let's make Atlas out of spaghetti and the boulder a meatball, to fit more with what Jboogx has been doing on the food side of things lately (shoutout to our good friend James Gerde -@gerdegotit @gerdedoesit @gerdemadeit, who did the first spaghetti animation). Since I was going to record myself, it wouldn't have been right if Jboogx didn't as well! This is just a taste of what’s possible. I'm located in Germany, and Jboogx is in Hawaii. A world apart, and we pulled this together in 48 hours. BTS coming soon 😉

“The Last Meatball” Animation: jboogx.creative & enigmatic_e Movement: jboogx.creative & enigmatic_e Reference Imagery: FLUX on Civitai Jboogx and I have known each other for the better part of the last 12 months. I think we've both been inspired by each other's work and dedication to exploring all the ways we can push these tools to their limits for VFX and animation. We didn't want to force a collaboration, but recently, Jboogx was inspired by the work I've been doing and how I incorporate myself into my animations. This idea is a playful take on 'Atlas carrying the boulder,' but with a twist—let's make Atlas out of spaghetti and the boulder a meatball, to fit more with what Jboogx has been doing on the food side of things lately (shoutout to our good friend James Gerde -@gerdegotit @gerdedoesit @gerdemadeit, who did the first spaghetti animation). Since I was going to record myself, it wouldn't have been right if Jboogx didn't as well! This is just a taste of what’s possible. I'm located in Germany, and Jboogx is in Hawaii. A world apart, and we pulled this together in 48 hours. BTS coming soon 😉

enigmatic_e

11,422 次观看 • 1 年前

Something NVIDIA & Google do better than anyone else is software-hardware-system co-design, and not just optimizing hardware for current model architectures, but predicting future ones. Back in early 2022, when NVIDIA started the design process for NVL72, MoE (Mixture of Experts) models were not yet the standard, and dense models were still dominant for frontier models. However, NVIDIA's strong software-hardware co-design culture enabled them to make a calculated bet that MoEs were the future, and they built NVL72 specifically for best MoE performance per TCO (Total Cost of Ownership). Furthermore, back in 2022, disaggregated prefill and wide expert parallelism (wideEP) MoE inference optimizations hadn't been invented yet, but it turns out that these MoE inference optimizations work best on large-scale systems like NVL72. While most other AI chip companies' in-house AI labs focus on training small 5B models that mainly use data parallelism, NVIDIA and Google's in-house AI labs continuously push the boundaries of model architecture and training recipes, such as NVFP4 training. Just like Super Idol & IShowSpeed, there must be a strong partnership between software engineers and hardware engineers to deliver the best systems that maximize performance per TCO.

Something NVIDIA & Google do better than anyone else is software-hardware-system co-design, and not just optimizing hardware for current model architectures, but predicting future ones. Back in early 2022, when NVIDIA started the design process for NVL72, MoE (Mixture of Experts) models were not yet the standard, and dense models were still dominant for frontier models. However, NVIDIA's strong software-hardware co-design culture enabled them to make a calculated bet that MoEs were the future, and they built NVL72 specifically for best MoE performance per TCO (Total Cost of Ownership). Furthermore, back in 2022, disaggregated prefill and wide expert parallelism (wideEP) MoE inference optimizations hadn't been invented yet, but it turns out that these MoE inference optimizations work best on large-scale systems like NVL72. While most other AI chip companies' in-house AI labs focus on training small 5B models that mainly use data parallelism, NVIDIA and Google's in-house AI labs continuously push the boundaries of model architecture and training recipes, such as NVFP4 training. Just like Super Idol & IShowSpeed, there must be a strong partnership between software engineers and hardware engineers to deliver the best systems that maximize performance per TCO.

SemiAnalysis

51,021 次观看 • 8 个月前

Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians paper page: Creating high-fidelity 3D head avatars has always been a research hotspot, but there remains a great challenge under lightweight sparse view setups. In this paper, we propose Gaussian Head Avatar represented by controllable 3D Gaussians for high-fidelity head avatar modeling. We optimize the neutral 3D Gaussians and a fully learned MLP-based deformation field to capture complex expressions. The two parts benefit each other, thereby our method can model fine-grained dynamic details while ensuring expression accuracy. Furthermore, we devise a well-designed geometry-guided initialization strategy based on implicit SDF and Deep Marching Tetrahedra for the stability and convergence of the training procedure. Experiments show our approach outperforms other state-of-the-art sparse-view methods, achieving ultra high-fidelity rendering quality at 2K resolution even under exaggerated expressions.

AK

65,853 次观看 • 2 年前

WE'VE BEEN SITTING ON A SECRET... 🤫 Wishing everyone a happy #InternationalDayOfCharity and also, a message: Don't ever think you can't make a difference. For each other, for others, for people you'll never meet, for causes. The Fallout community ceased being a community long ago, because in actuality - we're a found family. We hail from all over the world, come from all walks of life, and we all found home in the strangest of places: the end. Since #FalloutForHope began this community has raised almost $1 million for those in need and we've helped each other when we've needed it. That's family. We're thrilled to share that this October 23rd, we're going "home" for a holiday that embodies the spirit of our community. This #FalloutDay, for the first time, we'll be live from Bethesda in Rockville with Scottykfitness and we can't wait for you to see what we have planned... Please stand by. Looking to get involved? Start here 👇

WE'VE BEEN SITTING ON A SECRET... 🤫 Wishing everyone a happy #InternationalDayOfCharity and also, a message: Don't ever think you can't make a difference. For each other, for others, for people you'll never meet, for causes. The Fallout community ceased being a community long ago, because in actuality - we're a found family. We hail from all over the world, come from all walks of life, and we all found home in the strangest of places: the end. Since #FalloutForHope began this community has raised almost $1 million for those in need and we've helped each other when we've needed it. That's family. We're thrilled to share that this October 23rd, we're going "home" for a holiday that embodies the spirit of our community. This #FalloutDay, for the first time, we'll be live from Bethesda in Rockville with Scottykfitness and we can't wait for you to see what we have planned... Please stand by. Looking to get involved? Start here 👇

FalloutForHope 🔜 GCX @ Universal Orlando Resort

44,749 次观看 • 11 个月前